Expected Regret Minimization for Bayesian Optimization with Student's-t Processes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
18 Downloads (Pure)

Abstract

Student's-t Processes were recently proposed as a probabilistic alternative to Gaussian Processes for Bayesian optimization. Student's-t Processes are a generalization of Gaussian Processes, using an extra parameter v, which addresses Gaussian Processes' weaknesses. Separately, recent work used prior knowledge of a black-box function's global optimum f*, to create a new acquisition function for Bayesian optimization called Expected Regret Minimization. Gaussian Processes were then combined with Expected Regret Minimization to outperform existing models for Bayesian optimization. No published work currently exists for Expected Regret Minimization with Student's-t Processes. This research compares Expected Regret Minimization for Bayesian optimization, using Student's-t Processes versus Gaussian Processes. Both models are applied to four problems popular in mathematical optimization. Our work enhances Bayesian optimization by showing superior training regret minimization for Expected Regret Minimization, using Student's-t Processes versus Gaussian Processes.
Original languageEnglish
Title of host publicationAIPR 2020: Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
PublisherAssociation for Computing Machinery
Pages8-12
VolumeJune 2020
ISBN (Print) 9781450375511
DOIs
Publication statusPublished - 26 Jun 2020
Event2020 3rd International Conference on Artificial Intelligence and Pattern Recognition - Online, Xiamen, China
Duration: 26 Jun 202028 Jun 2020

Publication series

NameProceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
PublisherAssociation for Computing Machinery

Conference

Conference2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
Abbreviated titleAIPR 2020
Country/TerritoryChina
CityXiamen
Period26/06/2028/06/20

Fingerprint

Dive into the research topics of 'Expected Regret Minimization for Bayesian Optimization with Student's-t Processes'. Together they form a unique fingerprint.

Cite this